Embodiments of the present disclosure generally relate to neurostimulation (NS) systems, and more particularly to model-based programming guidance for implantation of spinal cord stimulation (SCS) systems.
NS systems are devices that generate electrical pulses and deliver the pulses to nervous tissue to treat a variety of disorders. For example, SCS has been used to treat chronic and intractable pain. Another example is deep brain stimulation, which has been used to treat movement disorders such as Parkinson's disease and affective disorders such as depression. While a precise understanding of the interaction between the applied electrical energy and the nervous tissue is not fully appreciated, it is known that application of electrical pulses depolarize neurons and generate propagating action potentials into certain regions or areas of nerve tissue. The propagating action potentials effectively mask certain types of pain transmitted from regions, increase the production of neurotransmitters, or the like. For example, applying electrical energy to the spinal cord associated with regions of the body afflicted with chronic pain can induce “paresthesia” (a subjective sensation of numbness or tingling) in the afflicted bodily regions. Inducing this artificial sensation replaces the feeling of pain in the body areas effectively masking the transmission of non-acute pain sensations to the brain.
Computational modeling of SCS, through coupled three dimensional (3-D) electrical field and nerve fiber kinetic models, can provide a tool for assessing the effectiveness of the SCS and/or a placement of the NS system within the patient. However, current modeling approaches commonly require commercial software packages that involve computationally-intensive steps, such as: obtain magnetic resonance imaging (MRI) of the patient; perform tissue segmentation on the medical images to create a 3-D spinal cord (SC) geometrical model; position the implanted leads within the SC model; specify stimulation contacts on the lead and set boundaries to contact-voltage/current condition; mesh the models; and in two stages solve for the electrical fields and activation regions in the dorsal column (DC) and dorsal root (DR) of the SC; and determine stimulation thresholds and activated dermatomal fiber zones. The process requires multiple software packages and specialized personnel to perform the tasks, which make conventional modeling approach difficult in the clinical setting.
However, some of the SCS systems available are not MRI-compatible, requiring MRI images to be taken prior to implant of the SCS system, and other modalities (e.g., X-rays, computed tomography (CT) scan) are needed to determine SCS lead position after implant. Moreover, detailed SC anatomy is difficult to ascertain with clinical MRI sequences, with dermatomal fiber tracts from such MRI images being difficult to visualize. Further, solving the computational model with the conventional approach is time-consuming, making this difficult to use in the clinical setting during an office visit or SCS implant. A need exists to overcome the shortcomings of traditional modeling methods.